• The Korean Journal of Physiology
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• v.20 no.1
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• pp.53-63
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• 1986

Carbon monoxide(CO) poisoning has been one of the major environmental problems because of the tissue hypoxia, especially brain tissue hypoxia, due to the great affinity of CO with hemoglobin. Inhalation of the pure oxygen$(0_2)$ under the high atmospheric pressure has been considered as the best treatment of CO poisoning by the supply of $0_2$ to hypoxic tissues with dissolved from in plasma and also by the rapid elimination of CO from the carboxyhemoglobin(HbCO). Hydrogen peroxide $(H_2O_2)$ was rapidly decomposed to water and $0_2$ under the presence of catalase in the blood, but the intravenous administration of $H_2O_2$ is hazardous because of the formation of methemoglobin and air embolism. However, it was reported that the enema of $H_2O_2$ solution below 0.75% could be continuously supplied $0_2$ to hypoxic tissues without the hazards mentioned above. This study was performed to evaluate the effect of $H_2O_2$ enema on the elimination of CO from the HbCO in the recovery of the acute CO poisoning. Rabbits weighting about 2.0 kg were exposed to If CO gas mixture with room air for 30 minutes. After the acute CO poisoning, 30 rabbits were divided into three groups relating to the recovery period. The first group T·as exposed to the room air and the second group w·as inhalated with 100% $0_2$ under 1 atmospheric pressure. The third group was administered 10 ml of 0.5H $H_2O_2$ solution per kg weight by enema immediately after CO poisoning and exposed to the room air during the recovery period. The arterial blood was sampled before and after CO poisoning ana in 15, 30, 60 and 90 minutes of the recovery period. The blood pH, $Pco_2\;and\;Po_2$ were measured anaerobically with a Blood Gas Analyzer and the saturation percentage of HbCO was measured by the Spectrophotometric method. The effect of $H_2O_2$ enema on the recovery from the acute CO poisoning was observed and compared with the room air group and the 100% $0_2$ inhalation group. The results obtained from the experiment are as follows: The pH of arterial blood was significantly decreased after CO poisoning and until the first 15 minutes of the recovery period in all groups. Thereafter, it was slowly increased to the level of the before CO poisoning, but the recovery of pH of the $H_2O_2$ enema group was more delayed than that of the other groups during the recovery period. $Paco_2$ was significantly decreased after CO poisoning in all groups. Boring the recovery Period, $Paco_2$ of the room air group was completely recovered to the level of the before CO Poisoning, but that of the 100% $O_2$ inhalation group and the $H_2O_2$ enema group was not recovered until the 90 minutes of the recovery period. $Paco_2$ was slightly decreased after CO poisoning. During the recovery Period, it was markedly increased in the first 15 minutes and maintained the level above that before CO Poisoning in all groups. Furthermore $Paco_2$ of the $H_2O_2$ enema group was 102 to 107 mmHg and it was about 10 mmHg higher than that of the room air group during the recovery period. The saturation percentage of HbCO was increased up to the range of 54 to 72 percents after CO poisoning and in general it was generally diminished during the recovery period. However in the $H_2O_2$ enema group the diminution of the saturation percentage of HbCO was generally faster than that of the 100% $O_2$ inhalation group and the room air group, and its diminution in the 100% $O_2$ inhalation group was also slightly faster than that of the room air group at the relatively later time of the recovery period. In conclusion, the enema of 0.5% $H_2O_2$ solution is seems to facilitate the elimination of CO from the HbCO in the blood and increase $Paco_2$ simultaneously during the recovery period of the acute CO poisoning.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.157-175
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• 2013

Since Korean government announced 'Smartwork promotion strategy' in 2010, Korean firms and government organizations have started to adopt smartwork. However, the smartwork has been implemented only in a few of large enterprises and government organizations rather than SMEs (small and medium enterprises). In USA, both Yahoo! and Best Buy have stopped their flexible work because of its reported low productivity and job loafing problems. In addition, according to the literature on smartwork, we could draw obstacles of smartwork adoption and categorize them into the three types: institutional, organizational, and technological. The first category of smartwork adoption obstacles, institutional, include the difficulties of smartwork performance evaluation metrics, the lack of readiness of organizational processes, limitation of smartwork types and models, lack of employee participation in smartwork adoption procedure, high cost of building smartwork system, and insufficiency of government support. The second category, organizational, includes limitation of the organization hierarchy, wrong perception of employees and employers, a difficulty in close collaboration, low productivity with remote coworkers, insufficient understanding on remote working, and lack of training about smartwork. The third category, technological, obstacles include security concern of mobile work, lack of specialized solution, and lack of adoption and operation know-how. To overcome the current problems of smartwork in reality and the reported obstacles in literature, we suggest a novel smartwork service model based on NFC(Near Field Communication). This paper suggests NFC-based Smartwork Service Model composed of NFC-based Smartworker networking service and NFC-based Smartwork space management service. NFC-based smartworker networking service is comprised of NFC-based communication/SNS service and NFC-based recruiting/job seeking service. NFC-based communication/SNS Service Model supplements the key shortcomings that existing smartwork service model has. By connecting to existing legacy system of a company through NFC tags and systems, the low productivity and the difficulty of collaboration and attendance management can be overcome since managers can get work processing information, work time information and work space information of employees and employees can do real-time communication with coworkers and get location information of coworkers. Shortly, this service model has features such as affordable system cost, provision of location-based information, and possibility of knowledge accumulation. NFC-based recruiting/job-seeking service provides new value by linking NFC tag service and sharing economy sites. This service model has features such as easiness of service attachment and removal, efficient space-based work provision, easy search of location-based recruiting/job-seeking information, and system flexibility. This service model combines advantages of sharing economy sites with the advantages of NFC. By cooperation with sharing economy sites, the model can provide recruiters with human resource who finds not only long-term works but also short-term works. Additionally, SMEs (Small Medium-sized Enterprises) can easily find job seeker by attaching NFC tags to any spaces at which human resource with qualification may be located. In short, this service model helps efficient human resource distribution by providing location of job hunters and job applicants. NFC-based smartwork space management service can promote smartwork by linking NFC tags attached to the work space and existing smartwork system. This service has features such as low cost, provision of indoor and outdoor location information, and customized service. In particular, this model can help small company adopt smartwork system because it is light-weight system and cost-effective compared to existing smartwork system. This paper proposes the scenarios of the service models, the roles and incentives of the participants, and the comparative analysis. The superiority of NFC-based smartwork service model is shown by comparing and analyzing the new service models and the existing service models. The service model can expand scope of enterprises and organizations that adopt smartwork and expand the scope of employees that take advantages of smartwork.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Korean Journal of Poultry Science
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• v.17 no.3
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• pp.141-156
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• 1990

The experiments were conducted to investigate the possibility of improving the effectiveness of the existing method to estimate the edible meat weight in the live broiler chicken. A total of 360 birds, five male and female chicks from each line were sacrificed at Trial 1 (body weight 900-1, 000g), Trial 2 (body weight 1.200-1, 400g), Trial 3(body weight 1, 600-1, 700), and Trial 4(body weight 2, 000g) in order to measure the body weight, edible meat weight of breast, thigh and drumsticks, and various components of body weight. Each line was reared at the Poultry Breeding Farm, Seoul National University from the second of july, 1987 to the thirteenth of September, 1987. The results obtained from this study were summarized as follows ： 1. The average body weights of each line( H. T, M, A) were $2150.5\pm$34.9, $2133.0\pm$26.2, $1960.0\pm$23.1, and $2319.3\pm$27.9, respectively. at 7 weeks of age. The feed to body weight eain ratio for each line chicks was 2.55, 2.13, 2.08, and 2.03, respectively, for 0 to 7 weeks of age. The viability of each line was 99.7. 99.7, 100.0, and 100.0%, respectively, for 0 to 7 weeks of age.01 was noticed that A Line chicks grow significantly heavier than did T, H, M line chic ks from 0 to 7 weeks of age. The regression coefficients of growth curves from each line chicks were bA＝1.015, bH＝0.265, bM＝0.950 and bT＝0.242, respectively. 2. Among the body weight components, the feather. abdominal fat, breast, and thigh and drumsticks increased in their weight percentage as the birds grew older, while neck. head, giblets and inedible viscera decreased. No difference wat apparent in shank, wings and hack. 3. The weight percentages of breast in edible part for each line thicks were 19.2, 19.0, 19.9 and 19.0% at Trial 4, respectively. The weight percentages of thigh and drumsticks in edible part for each line chicks were 23.1, 23.3, 22.8, and 23.0% at Trial 4. respective1y. 4. The values for the percentage meat yield from breast were 77.2. 78.9 73.5 and 74.8% at Trial 4 in H, T, M and A Line chicks. respectively. For thigh and drumstick, the values of 80.3, 78.4. 79.7 and 80.2% were obtained. These data indicate that the percentage meat yield increase as the birds grow older. 5. The correlation coefficients between body weight and blood. head, shanks. breast. thigh-drumstick were high. The degree if correlation between abdominal fat(%) and percentage of edible meat were extremely low at all times, but those between abdominal fat (%) and inedible viscera were significantly high.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.5
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• pp.321-327
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• 2006

In Korea, there is a growing competitive for water resources between industrial, domestic and agricultural consumer, and the environment as many other OECD countries. The demand on water use is also affecting aquatic ecosystems particularly where withdrawals are in excess of minimum environmental needs for rivers, lakes and wetland habits. OECD developed three indicators related to water use by the agriculture in above contexts : the first is a water use intensity indicator, which is expressed as the quantity or share of agricultural water use in total national water utilization; the second is a water stress indicator, which is expressed as the proportion of rivers (in length) subject to diversion or regulation for irrigation without reserving a minimum of limiting reference flow; and the third is a water use efficiency indicator designated as the technical and the economic efficiency. These indicators have different meanings in the aspect of water resource conservation and sustainable water use. So, it will be more significant that the indicators should reflect the intrinsic meanings of them. The problem is that the aspect of an overall water flow in the agro-ecosystem and recycling of water use not considered in the assessment of agricultural water use needed for calculation of these water use indicators. Namely, regional or meteorological characteristics and site-specific farming practices were not considered in the calculation of these indicators. In this paper, we tried to calculate water use indicators suggested in OECD and to modify some other indicators considering our situation because water use pattern and water cycling in Korea where paddy rice farming is dominant in the monsoon region are quite different from those of semi-arid regions. In the calculation of water use intensity, we excluded the amount of water restored through the ground from the total agricultural water use because a large amount of water supplied to the farm was discharged into the stream or the ground water. The resultant water use intensity was 22.9% in 2001. As for water stress indicator, Korea has not defined nor monitored reference levels of minimum flow rate for rivers subject to diversion of water for irrigation. So, we calculated the water stress indicator in a different way from OECD method. The water stress indicator was calculated using data on the degree of water storage in agricultural water reservoirs because 87% of water for irrigation was taken from the agricultural water reservoirs. Water use technical efficiency was calculated as the reverse of the ratio of irrigation water to a standard water requirement of the paddy rice. The efficiency in 2001 was better than in 1990 and 1998. As for the economic efficiency for water use, we think that there are a lot of things to be taken into considerations to make a useful indicator to reflect socio-economic values of agricultural products resulted from the water use. Conclusively, site-specific, regional or meteorogical characteristics as in Korea were not considered in the calculation of water use indicators by methods suggested in OECD(Volume 3, 2001). So, it is needed to develop a new indicators for the indicators to be more widely applicable in the world.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.155-166
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• 2002

Since 1990s. there has been an increasing number of traffic accidents at intersection. which requires more urgent measures to insure safety on intersection. This study set out to analyze the road conditions, traffic conditions and traffic operation conditions on signalized intersection. to identify the elements that would impose obstructions in safety, and to develop a traffic accident prediction model to evaluate the safety of an intersection using the cop relation between the elements and an accident. In addition, the focus was made on suggesting appropriate traffic safety policies by dealing with the danger elements in advance and on enhancing the safety on the intersection in developing a traffic accident prediction model fir a signalized intersection. The data for the study was collected at an intersection located in Wonju city from January to December 2001. It consisted of the number of accidents, the road conditions, the traffic conditions, and the traffic operation conditions at the intersection. The collected data was first statistically analyzed and then the results identified the elements that had close correlations with accidents. They included the area pattern, the use of land, the bus stopping activities, the parking and stopping activities on the road, the total volume, the turning volume, the number of lanes, the width of the road, the intersection area, the cycle, the sight distance, and the turning radius. These elements were used in the second correlation analysis. The significant level was 95% or higher in all of them. There were few correlations between independent variables. The variables that affected the accident rate were the number of lanes, the turning radius, the sight distance and the cycle, which were used to develop a traffic accident prediction model formula considering their distribution. The model formula was compared with a general linear regression model in accuracy. In addition, the statistics of domestic accidents were investigated to analyze the distribution of the accidents and to classify intersections according to the risk level. Finally, the results were applied to the Spearman-rank correlation coefficient to see if the model was appropriate. As a result, the coefficient of determination was highly significant with the value of 0.985 and the ranks among the intersections according to the risk level were appropriate too. The actual number of accidents and the predicted ones were compared in terms of the risk level and they were about the same in the risk level for 80% of the intersections.